class_docstr: Bollinger Bands filter as described in http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:bollinger_bands.
  
  :param dataSeries: The DataSeries instance being filtered.
  :type dataSeries: :class:`pyalgotrade.dataseries.DataSeries`.
  :param period: The number of values to use in the calculation. Must be > 1.
  :type period: int.
  :param numStdDev: The number of standard deviations to use for the upper and lower bands.
  :type numStdDev: int.
  :param maxLen: The maximum number of values to hold.
      Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the
      opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.
  :type maxLen: int.
docstr: Returns the middle band as a :class:`pyalgotrade.dataseries.DataSeries`.
docstr_tok: ["Returns","the","middle","band","as","a",":","class",":","`","pyalgotrade.dataseries.DataSeries","`","."]
code: def getMiddleBand(self):
      """
      Returns the middle band as a :class:`pyalgotrade.dataseries.DataSeries`.
      """
      return self.__sma
  
file_path: pyalgotrade/pyalgotrade/technical/bollinger.py
imports: from pyalgotrade import dataseries
  from pyalgotrade.technical import ma
  from pyalgotrade.technical import stats
label_desc: Use image node_id 4 for calling the BollingerBands obj's underlying member method code with example usage: obj.getMiddleBand() and returns: self